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Int. J. Six Sigma and Competitive Advantage, Vol. 7, Nos. 2/3/4, 2012 243 Copyright © 2012 Inderscience Enterprises Ltd. Six Sigma implementation to minimise weight variation for Baby Lido Diaper manufacturing company Nabeel Mandahawi* and Suleiman M. Obeidat Industrial Engineering Department, The Hashemite University, Zarqa 13115, Jordan E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: The increasing demand in baby diaper products in addition to customer requirements has compelled companies to improve the quality of their production processes. This paper presents a customised Six Sigma procedure to reduce weight variation in diaper products. To this end, a define, measure, analyse, improve and control (DMAIC) model is developed and applied at a production line at a local company to identify and control root causes. Thorough analysis of collected data revealed that excess fluff, sap and pulp are the main causes for variations. As a result, several changes have been adopted at the improvement phase; these improvements decreased defects per million opportunities (DPMO) form 292,157 to 88,152 and increased the sigma level from 2.05 to 2.85. Keywords: baby diaper product; Six Sigma; DMAIC methodology; weight variation reduction; quality improvement. Reference to this paper should be made as follows: Mandahawi, N. and Obeidat, S.M. (2012) ‘Six Sigma implementation to minimise weight variation for Baby Lido Diaper manufacturing company’, Int. J. Six Sigma and Competitive Advantage, Vol. 7, Nos. 2/3/4, pp.243–255. Biographical notes: Nabeel Mandahawi is an Associate Professor in the Department of Systems Science and Industrial Engineering at the Hashemite University. He received his PhD in Industrial Engineering from University of Texas at Arlington since 2005. His major is Operation Management and Human Factor Engineering. He has published more than 25 international journal and conference papers in these areas on many mature journals such as International Journal of Six Sigma and Competitive Advantage, Ergonomics, Journal of Human Ergology, International Journal of Industrial Ergonomics and others. Furthermore, He spent ten years working in some of the elite companies in Jordan and the USA, including names such as 2M Solutions Security Group, Jordan Light Vehicle Manufacturing and others. Suleiman M. Obeidat is an Assistant Professor in Industrial Engineering Department at the Hashemite University, Jordan. He earned his BSc in Mechanical Engineering from Jordan University of Science and Technology, Jordan, in 1996 and MSc in Mechanical Engineering from University of Jordan, Jordan, in 2000. He graduated from University of Oklahoma, Norman, Oklahoma, USA in May 2008 with a PhD in Industrial Engineering. His research interests include design and manufacturing materials, CAD/CAM,

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Page 1: Six Sigma implementation to minimise weight variation for Baby …€¦ · Six Sigma implementation to minimise weight variation for Baby Lido Diaper 245 2 Case study Since 1970s,

Int. J. Six Sigma and Competitive Advantage, Vol. 7, Nos. 2/3/4, 2012 243

Copyright © 2012 Inderscience Enterprises Ltd.

Six Sigma implementation to minimise weight variation for Baby Lido Diaper manufacturing company

Nabeel Mandahawi* and Suleiman M. Obeidat Industrial Engineering Department, The Hashemite University, Zarqa 13115, Jordan E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: The increasing demand in baby diaper products in addition to customer requirements has compelled companies to improve the quality of their production processes. This paper presents a customised Six Sigma procedure to reduce weight variation in diaper products. To this end, a define, measure, analyse, improve and control (DMAIC) model is developed and applied at a production line at a local company to identify and control root causes. Thorough analysis of collected data revealed that excess fluff, sap and pulp are the main causes for variations. As a result, several changes have been adopted at the improvement phase; these improvements decreased defects per million opportunities (DPMO) form 292,157 to 88,152 and increased the sigma level from 2.05 to 2.85.

Keywords: baby diaper product; Six Sigma; DMAIC methodology; weight variation reduction; quality improvement.

Reference to this paper should be made as follows: Mandahawi, N. and Obeidat, S.M. (2012) ‘Six Sigma implementation to minimise weight variation for Baby Lido Diaper manufacturing company’, Int. J. Six Sigma and Competitive Advantage, Vol. 7, Nos. 2/3/4, pp.243–255.

Biographical notes: Nabeel Mandahawi is an Associate Professor in the Department of Systems Science and Industrial Engineering at the Hashemite University. He received his PhD in Industrial Engineering from University of Texas at Arlington since 2005. His major is Operation Management and Human Factor Engineering. He has published more than 25 international journal and conference papers in these areas on many mature journals such as International Journal of Six Sigma and Competitive Advantage, Ergonomics, Journal of Human Ergology, International Journal of Industrial Ergonomics and others. Furthermore, He spent ten years working in some of the elite companies in Jordan and the USA, including names such as 2M Solutions Security Group, Jordan Light Vehicle Manufacturing and others.

Suleiman M. Obeidat is an Assistant Professor in Industrial Engineering Department at the Hashemite University, Jordan. He earned his BSc in Mechanical Engineering from Jordan University of Science and Technology, Jordan, in 1996 and MSc in Mechanical Engineering from University of Jordan, Jordan, in 2000. He graduated from University of Oklahoma, Norman, Oklahoma, USA in May 2008 with a PhD in Industrial Engineering. His research interests include design and manufacturing materials, CAD/CAM,

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244 N. Mandahawi and S.M. Obeidat

FEA, CAD directed inspection, CAD tissue engineering, engineering education, and statistical analysis. He is a member in Alpha Pi Mu, the National Industrial Engineering Honour Society.

1 Introduction

Globalisation has put today’s organisations into new strategic practices to stay competitive in the market place based on quality, variety, innovation, cost and time. Therefore, companies invest in improving their processes to enhance quality and reduce costs across the entire supply chain which includes small and medium size companies. However, these companies do not have the required resources to tune to automation nor the level of integration required for such efforts to improve their processes. Today, Six Sigma is characterised the latest management trend for improving tools and techniques (Watson, 2006); it helps organisations achieve their strategic goals through a project driven approach (Coronado and Antony, 2002) through the collection, analyses and display of data in a consistent manner (Maleyeff and Kaminsky, 2002). Six Sigma involves various areas of knowledge including project management, fault diagnosis and control (Yang and El-Haik, 2003). The concept of quality in Six Sigma implies that customers receive exactly what they want without defects (Stapenhurst, 2005). From a statistics point of view, Six Sigma’s goal is to eliminate defects up to 3.4 defects per million opportunities (DPMO) (Allen, 2006).

Six Sigma implementations depend on a systematic methodology of five steps known as define, measure, analyse, improve and control (DMAIC) methodology, Pyzdek (2003) and Keller (2005) provided a comprehensive description of this methodology. In the literature, researchers successfully implemented Six Sigma either alone or combined with other tools to improve performance and cut costs in the manufacturing and service sectors (Linderman et al., 2003). Recent research papers include improving operational safety (Cournoyer et al., 2010), increasing customer loyalty in the banking sector for Bank of America and Citigroup (Rucker, 2000; Roberts, 2004), reducing patients’ waiting time and length of stay (Mandahawi et al., 2010b), reducing length of stay for ophthalmology day case surgery (Mandahawi et al., 2010a) and increasing productivity in paper manufacturing (Mandahawi et al., 2012). In manufacturing, related literature include enhancing the efficiency of the assembly lines of military products (Cheng, 2005), increasing line capacity in automobile manufacturing for V8 monoblock engines (Jin et al., 2009), minimising the maximum inter cell flow (Shirazi et al., 2010), minimising rejection and rework in a honing process in an automobile part manufacturing company (Gijo and Scaria, 2010), minimising bush diameter variation in bicycle chain manufacturing (Kaushik et al., 2012), and decreasing percentage of defected parts caused by dents in the forging process (Liu, 2011). Moreover, Six Sigma has been used to improve the supply chain network at Malaysian manufacturing industries (Shirazi et al., 2010).

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2 Case study

Since 1970s, disposable diaper technology has continued to evolve. Today’s diapers are not only highly functional, but also it includes advanced features such as special sizing and colouring for specific gender and age, colour change indicators to show when the child is wet and re-attachable Velcro TM-type closures. These innovations have enabled disposables to capture a large share of diaper market. According to the World Health Organization (http://www.who.int/), very day in 1997 about 365,000 babies were born and today’s population is made up of 613 million children under age 5. Between 1995 to 2025, the population of children under 5 will grow by 0.25% annually.

Diaper (nappy) products advanced largely in the past decades and have attracted consumers for its functionality and various features. The core component of a disposable diaper is the absorbent pad composed of a hydrophilic polymer and a fibrous material. The polymer is made of fine particles that act as tiny sponges and are able to retain many times their weight in water. The absorbent pad is held in place by non-woven fabric sheets that form the body of the diaper. Components are assembled mechanically, chemically, or thermally. The large number of components contributes to variability in critical to quality characteristics of the diaper. In this study, a Six Sigma DMAIC model is used to minimise weight variation in diaper products at a local diaper manufacturing company. The following subsection illustrates how the DMAIC cycle is used.

3 The DMAIC model

Among the many products of the company, an ABC analysis of historical data show that medium size baby diapers are highly demanded which is widely manufactured in machine one. Building on customer complaints and verified by laboratory tests, the primary objective of this project is set to minimise weight variation in medium size disposable diapers. The company aims at increasing customer’s satisfaction by increasing sigma level within the specification limits.

3.1 Define

In the define phase, the project scope, primary and secondary objectives, counter balance, stakeholders, risks, work schedules, team members which include project champion; certified Six Sigma black belt; process owners, project leaders are clearly defined in the project charter. Table 1 shows the project charter used to guide this research. Project charter has been used in this phase to prevent any conflict that may occur between the team members. The charter was reviewed, discussed and modified by the DMAIC team, and was then approved by top management.

3.2 Measure

The first step in the measure phase is to identify the initial capability of the current process. To this end, a random sample of 510 diapers have been collected and weighed over a two months period covering the three production shifts. MINITAB was used to calculate the current sigma level, DPMO, mean and variation of the process as illustrated

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246 N. Mandahawi and S.M. Obeidat

in Figure 1. Capability analysis revealed that the current process output involves a 292,157 DPMO, a sigma level of 2.04, a process variation wider than specification limits and a shift in the process mean from its original target where the Process Capability Index (Cpk) and the Process Performance Index (Ppk) values were 0.2 and 0.16, respectively. Moreover, the variations between the weights of the individual diapers are illustrated in Figure 2 using the I-MR charts. The results show that the variations in the individual values are wide and high also the moving range between the values. The failure cost for the defaulted diapers has been divided into internal and external cost. The internal failure costs occur after appraisal and declarations of non-conforming diaper products and include cost of scrap when rework is no longer possible. Also, it includes costs of overtime to cover production losses, rescheduling and setup time. External failure cost occur when a non-confirming diaper products is erroneously delivered to the customer which cause loss of goodwill and sales, external cost is estimated by the company (Sharma et al., 2007). Table 1 Project charter

Problem statement: Minimise weight variation in disposable diapers by increasing the sigma level of the process.

Background: Data on current weight variation will be collected and analysed to measure current sigma level. A DMAIC model will then be developed to identify and control potential causes for existing weight variation and a new sigma level will be calculated.

Project primary objectives: Increase the sigma level of the process.

Project secondary objectives: Reduce production cost per diaper; increase customer satisfaction and reduce wastes.

Project counter balance: To maintain, at least, the current production rate.

Stakeholders departments: quality control, maintenance, raw material warehouse, production, production planning, finished good warehouse and marketing departments.

Project bounders (scope): The scope of this project will be limited to a predefined class of disposable diapers manufactured on a specified machine.

Management commitment: Top management is committed to provide the required data and to facilitate access to locations to allow collection of data.

Assumptions: All the required resources, including raw materials, are available during the production period.

To ensure that main source of variation is related to the manufacturing process and not due to the measurement process, a Gage R&R test has been performed with a focus on reproducibility. To this end, three team members were selected to twice weigh different samples of a size of 50 diapers each. Using MINITAB, obtained results showed that variation due to Gage R&R contributed to 22.69% of the total variation while the variation due to the samples were 77.31% as illustrated in Table 2. This indicates that measurements are acceptable and that variations are mainly due to variations in the inputs of the manufacturing process.

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Six Sigma implementation to minimise weight variation for Baby Lido Diaper 247

Figure 1 Current process capability analysis (see online version for colours)

Figure 2 I-MR chart of the current process (see online version for colours)

Observation

Ind

ivid

ua

l V

alu

e

510459408357306255204153102511

50

45

40

35

30

_X=36.08

UC L=40.42

LC L=31.74

Observation

Mo

vin

g R

an

ge

510459408357306255204153102511

15

10

5

0

__MR=1.63

UC L=5.34

LC L=0

1

1

1

1

111

11

111

1

1

1

1

11111

111

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248 N. Mandahawi and S.M. Obeidat

Table 2 Gage R&R results

Source VarComp Contribution (%)

Repeatability 0.00003 22.69 Reproducibility 0.34113 0.00 Total Gage R&R 0.34117 22.69 Part-to-part 1.16235 77.31 Total variation 1.50352 100.00

3.3 Analyse

The analysis phase aims at identifying sources of variation among the various inputs of the process. Figure 3 shows a detailed process map to have a clear view of the main production stages and the inputs at each stage required to produce a diaper, where the sigma level of the production process (Y) is affected mainly by the inputs of each stage (X’s).

Figure 3 Detailed process map for producing a diaper (see online version for colours)

Following a set of brainstorming sessions, a cause-and-effect matrix is constructed to illustrate the potential impacts of internal and external process inputs on weight variation and cost in addition to the performance of production rate which reflect the project counter balance objective. Table 3 shows team’s insight on the impact of individual inputs on performance measures of the process. Illustrated results show that pulp, super absorbable polymers (SAP), fluff, and core are the most likely inputs to have direct effects on primary and counter balanced objectives of the project.

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Table 3 Cause and effect matrix

Rating of importance 10 7 4 Process step

Process input Weight variation

Performance of production rate

Cost reduction

Total

Pulp 9 9 3 165 Water 1 0 0 10

Hammer the pulp

Labour 1 0 0 10 Fluff 9 9 3 165 SAP 9 9 3 165

Carrier 1 0 1 14

Form the core

Glue 0 9 3 75 Core 9 9 3 165

Poly ethylene sheet 0 1 1 11 Frontal tape 0 0 1 4 Hydrophilic 0 9 3 75 Hydrophobic 0 9 3 75

Lycra 0 3 1 25

Merge components

Glue 0 9 3 75 Web 0 1 0 7

Eva tape 0 1 1 11 Cut the web

Shear cut 0 3 1 25 Longitudinal unit 0 1 0 7 Fold the

diaper Final cut 1 3 0 31 Folded diaper 0 3 0 21 Pack the

product Nylon bags 0 0 1 4

At this stage, the four main factors should be investigated further in order to study the direct effect these parameters may have on weight variation. However, these parameters have an effect within sub-processes so it is difficult to stop the machine and take samples from inside the production line and between the sub processes, which will eventually stop the production line. Therefore, to avoid stopping the production line, the impact of each of the identified factors on the weight variation is indirectly investigated using failure mode and effect analysis (FMEA). Using the FMEA tool, potential failure modes for the parameters have been addressed, mainly in the form of shortage and excess in the supply of the material. Moreover, potential failure effects, potential causes and current control for each parameter have been brainstormed. Potential failure causes for each parameter are identified using computed RPN using the severity, occurrence, and detection for each cause. Table 4 presents sample FMEA for SAP and core. Subsequently, an ABC analysis for the RPN numbers have been performed and the parameters that have the highest impact have been addressed at this stage, an overall summary of the final results for the FMEA analysis based upon the ABC analysis show that the most important parameters are fluff, SAP and pulp as shown in Table 5.

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250 N. Mandahawi and S.M. Obeidat

Table 4 FMEA for SAP and core

Potential failure mode

Potential failure effects

Severity Potential causes Occurrence Current

control Detection RPN

10 Calibration error

7 Personnel 7 490

10 Block 5 Personnel 8 400 10 Mechanical

error 2 - 10 200

Weight reduction

10 Electrical error

2 - 10 200

9 Calibration error

7 Personnel 7 441

9 Block 5 Personnel 8 360

Diaper performance

error

9 Mechanical error

2 - 10 180

SAP shortage

9 Electrical error

2 - 10 180

10 Vacuum 2 - 10 200 10 Mechanical

error 1 Personnel 8 80

Weight increase

10 Pulp cutting knife

1 Personnel + sensor

6 60

10 Vacuum 2 - 10 200 10 Mechanical

error 1 Personnel 8 80

Cost increase

10 Pulp cutting knife

1 Personnel + sensor

6 60

Unsuitable cut

7 Mechanical error

1 Personnel 8 56

2 Vacuum 2 - 10 40

Excess core

Block 2 Mechanical

error 1 Personnel 8 16

Table 5 List of the potential causes of weight variation

Main cause Failure mode Potential failure causes

1 The duct gap was closing Fluff Excess 2 Disturbance in the filtering operations

(OSPREY room) Pulp Excess or shortage Amount of the moistening water SAP Excess by 50% or more Calibration errors

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3.4 Improve phase

In this phase, efforts are focused on generating solutions to eliminate or minimise the impacts of identified root causes on weight variation. Solution alternatives are evaluated with regard to cost, ease of implementation, and probability of accomplishment of results upon implementation. Brainstormed solutions included:

1 Changing the geometry and/or the material of the duct that forces the fluff out of the ‘hammer the pulp’ process to the core forming and OSPREY room. The new duct will prevent fluff from jamming due to enlarged diameter and finer coating of the inside surface of the duct, the new duct will prevent the fluff from jamming at the corners inside the duct. A side and an upper view of the old duct are illustrated in Figure 4.

Figure 4 (a) A side view of the old duct (b) An upper view of the old duct (see online version for colours)

(a) (b)

2 Replacing the current 1 cm wide stall by a 5 cm wide one to better control the disturbance in the filtering operations at the OSPREY room. This will prevent the OSPREY filter carpet from moving sideways due to the large vacuum forces inside the room. As a result, the operator will not need to frequently stop the machine to adjust the position of the carpet to control fluff the old and the new one are illustrated in Figure 5.

3 Installing a spray watering system to reduce the friction inside the ‘hammer the pulp’ process and to eliminate the build-up of static charges. Within the current process, operators decide the amount of water used for each type of pulp based on their experiences which vary from one operator to the other. The proposed spray watering system is automatically where the operator will only need to start/shut down the system without any adjustments as illustrated in Figure 6.

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252 N. Mandahawi and S.M. Obeidat

Figure 5 (a) A closer view of the old stall (b) The 1 cm diameter stall (c) The 5 cm diameter stall (see online version for colours)

(a)

(b) (c)

Figure 6 (a) The old spot watering system (b) The new watering system (spray) (see online version for colours)

(a) (b)

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Six Sigma implementation to minimise weight variation for Baby Lido Diaper 253

4 Installing a filter at the opening of the absorption pipe for the SAP to prevent large SAP molecules from being used with the fluff to manufacture the core. Large SAP particles usually form as a result of the humid atmosphere of the warehouse and affect the SAP absorption mechanism. As a result, the amount of SAP used will vary with the size of formed molecules that impacts the weight of the diaper.

5 Adding a detector to control the distribution of SAP in each diaper.

Figure 7 Capability analysis of the modified process (see online version for colours)

4 0 .03 9 .23 8 .43 7 .63 6 .83 6 .03 5 .23 4 .4

L S L Ta r g e t U S L

L S L 3 5T arg et 3 7U S L 3 9S am p le M ean 3 7 . 0 6 0 8S am p le N 1 4 1 8S tD ev (W i th in ) 1 . 0 8 3 1 6S tD ev (O v eral l ) 1 . 1 4 3 0 9

P r o c e s s D a t a

C p 0 . 6 2C p k 0 . 6 0

P p 0 . 5 8P p k 0 . 5 7C p m 0 . 5 8

O ve r a l l C a p a b i l i t y

C a p a b i l i t yP o t e n t i a l ( W i t h i n )

P P M < L S L 4 0 1 9 7 . 4 6P P M > U S L 4 7 9 5 4 . 8 7P P M T o tal 8 8 1 5 2 . 3 3

O b s e r ve d P e r f o r m a n c e

P P M < L S L 2 8 5 4 7 . 7 5P P M > U S L 3 6 7 0 1 . 8 0P P M T o tal 6 5 2 4 9 . 5 5

Ex p . W i t h i n P e r f o r m a n c e

W ith inO v eral l

Figure 8 I-MR Chart for the modified process (see online version for colours)

4 0 .03 9 .23 8 .43 7 .63 6 .83 6 .03 5 .23 4 .4

L S L Ta r g e t U S L

L S L 3 5T arg et 3 7U S L 3 9S am p le M ean 3 7 . 0 6 0 8S am p le N 1 4 1 8S tD ev (W ith in ) 1 . 0 8 3 1 6S tD ev (O v eral l ) 1 . 1 4 3 0 9

P r o c e s s D a t a

C p 0 . 6 2C p k 0 . 6 0

P p 0 . 5 8P p k 0 . 5 7C p m 0 . 5 8

O ve r a l l C a p a b i l i t y

C a p a b i l i t yP o t e n t i a l ( W i t h i n )

P P M < L S L 4 0 1 9 7 . 4 6P P M > U S L 4 7 9 5 4 . 8 7P P M T o tal 8 8 1 5 2 . 3 3

O b s e r ve d P e r f o r m a n c e

P P M < L S L 2 8 5 4 7 . 7 5P P M > U S L 3 6 7 0 1 . 8 0P P M T o tal 6 5 2 4 9 . 5 5

Ex p . W i t h i n P e r f o r m a n c e

W ith inO v eral l

Following management approval, suggested solutions were implemented using RACI matrix in order to identify who is/are responsible, accountant, consultant, and the informed person in addition to the expected start and finish dates for each

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254 N. Mandahawi and S.M. Obeidat

action. Furthermore, new data were collected to assess results. Random samples of 1,420 products were collected and the capability of the modified processes was analysed using MINITAB as shown in Figure 7. Implemented solutions contributed to increasing the sigma level from 2.05 to 2.85 which resulted in minimising DPMO from around 292,157 defects to about 88,152 defects. Furthermore, the Cpk has increased from 0.2 up to 0.6 and the Ppk increased from 0.16 up to 0.57. Figure 8 presents the I-MR charts for collected data where results illustrate the reduced variation in individual readings where control limits were 34–40 gm compared to 30–48 gm in the original setup.

3.5 Control

The control phase aims at sustaining the updated conditions through a set of controlled action plans. In this phase, the company would like to make sure that the processes continue to work well, produce desired output results and maintain quality level. A simple check sheet has been developed to be used within the total preventing maintenance programme of the company to ensure the functionality of the installed components. Furthermore, the new process capability and an updated quality control chart to monitor the weight variation of the baby diapers are determined. Standardised work procedures have been documented to make sure that all employees are trained and communicate the project’s results. The DMAIC team decided to meet periodically and reviewed control chart and process capability.

4 Conclusions

This paper presents an application of a DMAIC procedure to minimise weight variation for diaper products. Collected data showed that output from the current process involves a 292,157 DPMO, a current process sigma level of 2.05, a shifted process mean and high variability resulting in a Cpk of 0.16. Different tools have been used at the measure and the analysis phase in order to identify the main factors that contribute largely to increase weight variation for the current process. Thorough investigation revealed that excess fluff, SAP and excess or shortage pulp are the main causes for weight variation. Several technical modifications were adopted to reduce the impacts of identified causes. Implemented changes included changing the duct at the OSPREY room to avoid fluff jamming, using a 5 cm wide stall to control sideways movements of the OSPREY carpet, installing a spray watering system to reduce friction, installing a filter at the opening of the SAP duct to strain large SAP particles, and inserting a detector to control the distribution of SAP in the diaper. As a result, implemented changes contributed to reducing DPMO to 88,152 and to increasing the sigma level to 2.85 which offered a reduction in cost about $35,000 per year which is a satisfactory amount of saving from the top management perspective.

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